Sleuth: A Trace-Based Root Cause Analysis System for Large-Scale Microservices with Graph Neural Networks

Abstract

Cloud microservices are being scaled up due to the rising demand for new features and the convenience of cloud-native technologies. However, the growing scale of microservices complicates the remote procedure call (RPC) dependency graph, exacerbates the tail-of-scale effect, and makes many of the empirical rules for detecting the root cause of end-to-end performance issues unreliable. Additionally, existing open-source microservice benchmarks are too small to evaluate performance debugging algorithms at a production-scale with hundreds or even thousands of services and RPCs. To address these challenges, we present Sleuth, a trace-based root cause analysis (RCA) system for large-scale microservices using unsupervised graph learning. Sleuth leverages a graph neural network to capture the causal impact of each span in a trace, and trace clustering using a trace distance metric to reduce the amount of traces required for root cause localization. A pre-trained Sleuth model can be transferred to different microservice applications without any retraining or with few-shot fine-tuning. To quantitatively evaluate the performance and scalability of Sleuth, we propose a method to generate microservice benchmarks comparable to a production-scale. The experiments on the existing benchmark suites and synthetic large-scale microservices indicate that Sleuth has significantly outperformed the prior work in detection accuracy, performance, and adaptability on a large-scale deployment.